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Commit 6665b421 authored by Rockey's avatar Rockey Committed by GitHub
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[Fix] Fix the bug that when all pixels in an image is ignored, the ac… (#1336)

* [Fix] Fix the bug that when all pixels in an image is ignored, the accuracy calculation raises ZeroDivisionError

* use eps

* all close

* add ignore test

* add eps
parent f5e1f2e8
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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
......@@ -46,10 +47,13 @@ def accuracy(pred, target, topk=1, thresh=None, ignore_index=None):
correct = correct & (pred_value > thresh).t()
correct = correct[:, target != ignore_index]
res = []
eps = torch.finfo(torch.float32).eps
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(
correct_k.mul_(100.0 / target[target != ignore_index].numel()))
# Avoid causing ZeroDivisionError when all pixels
# of an image are ignored
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + eps
total_num = target[target != ignore_index].numel() + eps
res.append(correct_k.mul_(100.0 / total_num))
return res[0] if return_single else res
......
......@@ -56,50 +56,56 @@ def test_accuracy():
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=None)
acc = accuracy(pred, true_label)
assert acc.item() == 100
assert torch.allclose(acc, torch.tensor(100.0))
# test for ignore_index with a wrong prediction of that index
true_label = torch.Tensor([2, 3, 1, 1, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=1)
acc = accuracy(pred, true_label)
assert acc.item() == 100
assert torch.allclose(acc, torch.tensor(100.0))
# test for ignore_index 1 with a wrong prediction of other index
true_label = torch.Tensor([2, 0, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=1)
acc = accuracy(pred, true_label)
assert acc.item() == 75
assert torch.allclose(acc, torch.tensor(75.0))
# test for ignore_index 4 with a wrong prediction of other index
true_label = torch.Tensor([2, 0, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=4)
acc = accuracy(pred, true_label)
assert acc.item() == 80
assert torch.allclose(acc, torch.tensor(80.0))
# test for ignoring all the pixels
true_label = torch.Tensor([2, 2, 2, 2, 2]).long()
accuracy = Accuracy(topk=1, ignore_index=2)
acc = accuracy(pred, true_label)
assert torch.allclose(acc, torch.tensor(100.0))
# test for top1
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1)
acc = accuracy(pred, true_label)
assert acc.item() == 100
assert torch.allclose(acc, torch.tensor(100.0))
# test for top1 with score thresh=0.8
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, thresh=0.8)
acc = accuracy(pred, true_label)
assert acc.item() == 40
assert torch.allclose(acc, torch.tensor(40.0))
# test for top2
accuracy = Accuracy(topk=2)
label = torch.Tensor([3, 2, 0, 0, 2]).long()
acc = accuracy(pred, label)
assert acc.item() == 100
assert torch.allclose(acc, torch.tensor(100.0))
# test for both top1 and top2
accuracy = Accuracy(topk=(1, 2))
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
acc = accuracy(pred, true_label)
for a in acc:
assert a.item() == 100
assert torch.allclose(a, torch.tensor(100.0))
# topk is larger than pred class number
with pytest.raises(AssertionError):
......
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